Phase-sensitive spectral estimation by the hybrid filter diagonalization method

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• 作者：Hasan Celik ^{hcelik@uci.edu} ; Clark D. Ridge¹ ; ^{clarkridge@gmail.com} ; A.J. Shaka ; ^{ajshaka@uci.edu}
• 关键词：Filter diagonalization ; FDM ; Spectral estimation ; Lorentzian-to-Gaussian ; Phase-sensitive NMR spectra ; Resolution enhancement
• 刊名：Journal of Magnetic Resonance
• 出版年：2012
• 出版时间：January 2012
• 年：2012
• 卷：214
• 期：Complete
• 页码：15-21
• 全文大小：635 K

文摘

A more robust way to obtain a high-resolution multidimensional NMR spectrum from limited data sets is described. The Filter Diagonalization Method (FDM) is used to analyze phase-modulated data and cast the spectrum in terms of phase-sensitive Lorentzian ¡°phase-twist?peaks. These spectra are then used to obtain absorption-mode phase-sensitive spectra. In contrast to earlier implementations of multidimensional FDM, the absolute phase of the data need not be known beforehand, and linear phase corrections in each frequency dimension are possible, if they are required. Regularization is employed to improve the conditioning of the linear algebra problems that must be solved to obtain the spectral estimate. While regularization smoothes away noise and small peaks, a hybrid method allows the true noise floor to be correctly represented in the final result. Line shape transformation to a Gaussian-like shape improves the clarity of the spectra, and is achieved by a conventional Lorentzian-to-Gaussian transformation in the time-domain, after inverse Fourier transformation of the FDM spectra. The results obtained highlight the danger of not using proper phase-sensitive line shapes in the spectral estimate. The advantages of the new method for the spectral estimate are the following: (i) the spectrum can be phased by conventional means after it is obtained; (ii) there is a true and accurate noise floor; and (iii) there is some indication of the quality of fit in each local region of the spectrum. The method is illustrated with 2D NMR data for the first time, but is applicable to n-dimensional data without any restriction on the number of time/frequency dimensions.